1. I assume that the categorial moderator "occupation" (0=non-working; 1=working) influences only one relationship in my SEM.
Can I use the two-stage-approach in this case? Or would a multi-group-analysis appropriate? The problem here is that I will not have groups of the same size (data with 70% non-working records and 30% working records) ...

2. I assume that the categorial moderator "sex" (0=n-working; 1=working) influences all relationship in my SEM. Also here I will have the problem that my groups will not have the same size (20% male vs. 80% female). So I cannot use the permutation test. Is there another solution already installed in Smart Pls 3?

If your categorical moderator is supposed to influence just one relation than I would include it as dummy variable and use an interaction term to model the influence (using the two-stage approach).
If you categorical moderator is supposed to influence all relations in the model, I would probably revert to a Multi-Group-Analysis. As you have very unequal sample sizes, you should use the PLS-MGA implemented in SmartPLS. Nevertheless, you need to have sufficient sample size for a reliable estimation also in your small group. Otherwise, you will just not get trustworthy (good) results.

Thank you very much for your advice, Dr. Becker. May I ask you two additional questions in this context?

Is there a possibility to do the dummy transformation of the categorial variables in smart pls or first in SPSS and then import the dummy variables into smart pls? Dummy variable 1 would be e.g. "working" and dummy variable 2 would be "non-working".

Regarding the two-stage-approach with the dummy variables as moderators:
Do I first have to calculate the construct value of the moderator with the dummy variable 1 "working" and then use this value for the interaction term (multiplied with the value of my predictor?) ? And then again the same procedure (again two steps) with dummy variable 2 "non-working"? And then compare the two?

Or do I have the calculate the construct value of the moderator with the two dummy variable 1 + 2 at the same time and then use this value for the interaction term (multiplied with the value of my predictor?)

I am kind of confused because I do not know how to assign the dummy variables (they are neither reflective nor formative....).

You have to create your dummy variables outside of SmartPLS (e.g., in Excel or SPSS). SmartPLS cannot transform categorical variables into dummy variables.
If you have just two categories (e.g., "working" and "non-working") you will have just one dummy variable (e.g., 0="non-working", 1="working"). Only if you have more than two categories, you will have more than one dummy variable. Actually, you will always have (Number of Categories - 1) dummy variables. The reference category is zero for all dummies and the other (n-1 categories) are 1 for each of the dummies.

You would then include each dummy variable as a single-item construct into your SmartPLS model and create an interaction terms, where the moderator is the single-item construct.

Great, ok, it is much clearer now, Dr. Becker. Thank you very much for your answer. One question remains:

You wrote:
"You would then include each dummy variable as a single-item construct into your SmartPLS model and create an interaction terms, where the moderator is the single-item construct."

If I have e.g. three dummy variables and one reference category (e.g. age groups 1-4), do I have to include the three dummy variables as single-item-constructs at the same time in my model (moderators M1, M2, M3)) and then calculate just one interaction term (KW (Y1) x KW (M1)x KW (M2) x KW (M3))? Or include first only one dummy variable as moderator 1, calculate the corresponding interaction term and then the same procedure for the next dummy? So I would have 3 different interaction terms?

Sorry for bothering you, I would really appreciate your help once again.

It depends on what you want to test. Usually you want to investigate the first order interactions first. Higher-oder interactions (three-way or four-way interactions are really hard to interpret).
An interaction of the dummy with your predictor gives the change in the strength of your predictor onto the DV when you change from the reference category to the focal category (e.g., is there a weaker of stronger relation for age group 3 compared to age group 1 (reference category)).
You can of course also be interested in other tests (e.g., group 1&2 against 3&4 or 1 against 2&3&4, etc.) and construct other dummy coding schemes accordingly. However, knowing what you want to test is important here.